MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading | |
Cheng X(程曦)1,2; Zhang JH(张景昊)1,2; Ceng YN(曾宇楠)1,2; Xue WF(薛文芳)1,2 | |
2024-05 | |
会议日期 | 20240507-20240510 |
会议地点 | 台湾台北 |
英文摘要 | Algorithmic trading refers to executing buy and sell orders for specific assets based on automatically identified trading opportunities. Strategies based on reinforcement learning (RL) have demonstrated remarkable capabilities in addressing algorithmic trading problems. However, the trading patterns differ among market conditions due to shifted distribution data. Ignoring multiple patterns in the data will undermine the performance of RL. In this paper, we propose MOT, which designs multiple actors with disentangled representation learning to model the different patterns of the market. Furthermore, we incorporate the Optimal Transport (OT) algorithm to allocate samples to the appropriate actor by introducing a regularization loss term. Additionally, we propose Pretrain Module to facilitate imitation learning by aligning the outputs of actors with expert strategy and better balance the exploration and exploitation of RL. Experimental results on real futures market data demonstrate that MOT exhibits excellent profit capabilities while balancing risks. Ablation studies validate the effectiveness of the components of MOT. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/57130] ![]() |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | Xue WF(薛文芳) |
作者单位 | 1.中国科学院大学 2.中国科学院大学自动化研究所 |
推荐引用方式 GB/T 7714 | Cheng X,Zhang JH,Ceng YN,et al. MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading[C]. 见:. 台湾台北. 20240507-20240510. |
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